A. Botea

Research into health ICT adoption suggests that the failure to understand the clinical workplace has been a major contributing factor to the failure of many computer-based clinical systems. We suggest that clinicians and administrators need methods for envisioning future use when adopting new ICT. This paper presents and evaluates a six-stage "prospective evaluation" model that clinicians can use when assessing the impact of a new electronic patient information system on a Specialist Outpatients Department (SOPD). The prospective evaluation model encompasses normative, descriptive, formative and projective approaches. We show that this combination helped health informaticians to make reasonably accurate predictions for technology adoption at the SOPD. We suggest some refinements, however, to improve the scope and accuracy of predictions.

ABSTRACT In moving target search, the objective is to guide a hunter agent to catch a moving prey. Even though in game applications maps are always available at developing time, current approaches to moving target search do not exploit preprocessing to improve search performance. In this paper, we propose MtsCopa, an algorithm that exploits precomputed information in the form of compressed path databases (CPDs), and that is able to guide a hunter agent in both known and partially known terrain. CPDs have previously been used in standard, fixed-target pathfinding but had not been used in the context of moving target search. We evaluated MtsCopa over standard game maps. Our speed results are orders of magnitude better than current state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. Compared to state of the art, the number of hunter moves is often better and otherwise comparable, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA*, our method is simple to understand and implement. In addition, we prove MtsCopa always guides the agent to catch the prey when possible.

ABSTRACT Building efficient and sustainable transportation systems is a key challenge for accommodating the fast-increasing population living in cities. Lack of efficiency in transportation networks typically arises from uncertainty, e.g., about the availability of resources (such as parking lots or bicycles in bike sharing systems), or the exogenous factors affecting their demand (such as weather or the time of the day). In this paper, we present a class of algorithms which use Generalized Additive Models (GAMs) for demand and availability prediction on various time scales. In contrast to existing methods, exogenous effects can be explicitly factored into the models, resulting in significant gains in terms of prediction accuracy. Another advantage of our approach is that it estimates the distribution of the waiting time for the next available bike/parking lot if the current availability is zero. We showcase how this additional information can be used as part of personal uncertainty-aware journey planners which allow users to choose from multiple routes according to their time constraints.

We present new results in crossword composition, showing that our program significantly outperforms previous successful techniques in the literature. We emphasize phase transition phenomena, and identify classes of hard problems. Phase transition is shown to occur when varying problem parameters, such as the dictionary size and the number of blocked cells on a grid, of large-size realistic problems.

My research interests are in the areas of planning, computer games, and heuristic search. My objectives include using abstraction to solve hard heuristic search problems. Classical heuristic search has been successful for games like Chess and Checkers, but seems to be of limited value in games such as Go and Shogi, and puzzles such as Sokoban. My work is focused

Multi-agent path planning is a challenging problem with numerous real-life applications. Run-ning a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, as the state space size is exponential in the number ...